Bootstrapped training of event extraction classifiers Conference Paper uri icon


  • 2012 Association for Computational Linguistics. Most event extraction systems are trained with supervised learning and rely on a collection of annotated documents. Due to the domain-specificity of this task, event extraction systems must be retrained with new annotated data for each domain. In this paper, we propose a bootstrapping solution for event role filler extraction that requiresminimal human supervision. We aim to rapidly train a state-of-The-Art event extraction system using a small set of "seed nouns" for each event role, a collection of relevant (in-domain) and irrelevant (outof- domain) texts, and a semantic dictionary. The experimental results show that the bootstrapped system outperforms previous weakly supervised event extraction systems on the MUC-4 data set, and achieves performance levels comparable to supervised training with 700 manually annotated documents.

published proceedings

  • EACL 2012 - 13th Conference of the European Chapter of the Association for Computational Linguistics, Proceedings

author list (cited authors)

  • Huang, R., & Riloff, E.

complete list of authors

  • Huang, R||Riloff, E

publication date

  • January 2012